


import unittest

from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import (
			    FalconForCausalLM,
			    FalconForQuestionAnswering,
			    FalconForSequenceClassification,
			    FalconForTokenClassification,
			    FalconModel,
			)

class 							a						:




							"""simple docstring"""


							def __init__(				self			:    Tuple       ,      lowerCamelCase			:    str       ,      lowerCamelCase			:    Union[str, Any]=3       ,      lowerCamelCase			:    Optional[Any]=7       ,      lowerCamelCase			:    Dict=True       ,      lowerCamelCase			:    int=True       ,      lowerCamelCase			:    Optional[Any]=False       ,      lowerCamelCase			:    List[str]=True       ,      lowerCamelCase			:    Dict=99       ,      lowerCamelCase			:    int=32       ,      lowerCamelCase			:    List[Any]=5       ,      lowerCamelCase			:    str=4       ,      lowerCamelCase			:    Union[str, Any]=37       ,      lowerCamelCase			:    Optional[Any]="gelu"       ,      lowerCamelCase			:    List[str]=0.1       ,      lowerCamelCase			:    str=0.1       ,      lowerCamelCase			:    Optional[Any]=512       ,      lowerCamelCase			:    Tuple=16       ,      lowerCamelCase			:    List[Any]=2       ,      lowerCamelCase			:    Union[str, Any]=0.02       ,      lowerCamelCase			:    Dict=3       ,      lowerCamelCase			:    int=4       ,      lowerCamelCase			:    int=None       ,      )      ->       int:
														__snake_case						:     Any									=  parent
														__snake_case						:     Union[str, Any]									=  batch_size
														__snake_case						:     List[str]									=  seq_length
														__snake_case						:     Union[str, Any]									=  is_training
														__snake_case						:     Optional[Any]									=  use_input_mask
														__snake_case						:     Dict									=  use_token_type_ids
														__snake_case						:     Union[str, Any]									=  use_labels
														__snake_case						:     Any									=  vocab_size
														__snake_case						:     str									=  hidden_size
														__snake_case						:     Dict									=  num_hidden_layers
														__snake_case						:     Union[str, Any]									=  num_attention_heads
														__snake_case						:     Optional[Any]									=  intermediate_size
														__snake_case						:     int									=  hidden_act
														__snake_case						:     Dict									=  hidden_dropout_prob
														__snake_case						:     Optional[int]									=  attention_probs_dropout_prob
														__snake_case						:     Optional[Any]									=  max_position_embeddings
														__snake_case						:     Optional[int]									=  type_vocab_size
														__snake_case						:     Tuple									=  type_sequence_label_size
														__snake_case						:     Optional[int]									=  initializer_range
														__snake_case						:     Dict									=  num_labels
														__snake_case						:     Dict									=  num_choices
														__snake_case						:     Optional[int]									=  scope

							def        __snake_case   (				self			:    Union[str, Any]							)      ->       Optional[int]:
														__snake_case						:     List[str]									=  ids_tensor([self.batch_size, self.seq_length]       ,      self.vocab_size							)

														__snake_case						:     Tuple									=  None
														if self.use_input_mask:
																					__snake_case						:     Tuple									=  random_attention_mask([self.batch_size, self.seq_length]							)

														__snake_case						:     str									=  None

														__snake_case						:     str									=  None
														__snake_case						:     str									=  None
														__snake_case						:     List[str]									=  None
														if self.use_labels:
																					__snake_case						:     List[Any]									=  ids_tensor([self.batch_size]       ,      self.type_sequence_label_size							)
																					__snake_case						:     int									=  ids_tensor([self.batch_size, self.seq_length]       ,      self.num_labels							)
																					__snake_case						:     List[str]									=  ids_tensor([self.batch_size]       ,      self.num_choices							)

														__snake_case						:     Any									=  self.get_config()

														return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels

							def        __snake_case   (				self			:    str							)      ->       str:
														return FalconConfig(
														    vocab_size=self.vocab_size       ,      hidden_size=self.hidden_size       ,      num_hidden_layers=self.num_hidden_layers       ,      num_attention_heads=self.num_attention_heads       ,      intermediate_size=self.intermediate_size       ,      hidden_act=self.hidden_act       ,      hidden_dropout_prob=self.hidden_dropout_prob       ,      attention_probs_dropout_prob=self.attention_probs_dropout_prob       ,      max_position_embeddings=self.max_position_embeddings       ,      type_vocab_size=self.type_vocab_size       ,      is_decoder=__UpperCamelCase       ,      initializer_range=self.initializer_range       ,      pad_token_id=1       ,      new_decoder_architecture=__UpperCamelCase       ,      )

							def        __snake_case   (				self			:    Tuple       ,      lowerCamelCase			:    List[Any]       ,      lowerCamelCase			:    Dict       ,      lowerCamelCase			:    List[str]       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    Union[str, Any]       ,      lowerCamelCase			:    str       ,      lowerCamelCase			:    Optional[Any]							)      ->       Union[str, Any]:
														__snake_case						:     Any									=  FalconModel(config=__UpperCamelCase							)
														model.to(__UpperCamelCase							)
														model.eval()
														__snake_case						:     int									=  model(__UpperCamelCase       ,      attention_mask=__UpperCamelCase							)
														__snake_case						:     str									=  model(__UpperCamelCase							)
														self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)							)

							def        __snake_case   (				self			:    Optional[Any]       ,      lowerCamelCase			:    Tuple       ,      lowerCamelCase			:    List[Any]       ,      lowerCamelCase			:    Optional[int]       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    Optional[Any]       ,      lowerCamelCase			:    str       ,      lowerCamelCase			:    Dict       ,      lowerCamelCase			:    Optional[Any]       ,      lowerCamelCase			:    List[str]       ,      )      ->       Tuple:
														__snake_case						:     str									=  True
														__snake_case						:     List[str]									=  FalconModel(__UpperCamelCase							)
														model.to(__UpperCamelCase							)
														model.eval()
														__snake_case						:     Optional[int]									=  model(
														    __UpperCamelCase       ,      attention_mask=__UpperCamelCase       ,      encoder_hidden_states=__UpperCamelCase       ,      encoder_attention_mask=__UpperCamelCase       ,      )
														__snake_case						:     int									=  model(
														    __UpperCamelCase       ,      attention_mask=__UpperCamelCase       ,      encoder_hidden_states=__UpperCamelCase       ,      )
														__snake_case						:     Any									=  model(__UpperCamelCase       ,      attention_mask=__UpperCamelCase							)
														self.parent.assertEqual(result.last_hidden_state.shape       ,      (self.batch_size, self.seq_length, self.hidden_size)							)

							def        __snake_case   (				self			:    int       ,      lowerCamelCase			:    Optional[int]       ,      lowerCamelCase			:    Dict       ,      lowerCamelCase			:    List[Any]       ,      lowerCamelCase			:    Union[str, Any]       ,      lowerCamelCase			:    Tuple       ,      lowerCamelCase			:    Union[str, Any]       ,      lowerCamelCase			:    Any       ,      lowerCamelCase			:    List[Any]       ,      lowerCamelCase			:    int       ,      )      ->       Union[str, Any]:
														__snake_case						:     Tuple									=  FalconForCausalLM(config=__UpperCamelCase							)
														model.to(__UpperCamelCase							)
														model.eval()
														__snake_case						:     Any									=  model(__UpperCamelCase       ,      attention_mask=__UpperCamelCase       ,      labels=__UpperCamelCase							)
														self.parent.assertEqual(result.logits.shape       ,      (self.batch_size, self.seq_length, self.vocab_size)							)

							def        __snake_case   (				self			:    Dict       ,      lowerCamelCase			:    List[str]       ,      lowerCamelCase			:    Optional[Any]       ,      lowerCamelCase			:    List[str]       ,      lowerCamelCase			:    Union[str, Any]       ,      lowerCamelCase			:    Tuple       ,      lowerCamelCase			:    List[str]       ,      lowerCamelCase			:    Optional[Any]       ,      lowerCamelCase			:    Optional[Any]       ,      lowerCamelCase			:    Any       ,      )      ->       Union[str, Any]:
														__snake_case						:     List[str]									=  True
														__snake_case						:     Union[str, Any]									=  True
														__snake_case						:     Optional[int]									=  FalconForCausalLM(config=__UpperCamelCase							)
														model.to(__UpperCamelCase							)
														model.eval()

														# first forward pass
														__snake_case						:     Optional[Any]									=  model(
														    __UpperCamelCase       ,      attention_mask=__UpperCamelCase       ,      encoder_hidden_states=__UpperCamelCase       ,      encoder_attention_mask=__UpperCamelCase       ,      use_cache=__UpperCamelCase       ,      )
														__snake_case						:     str									=  outputs.past_key_values

														# create hypothetical multiple next token and extent to next_input_ids
														__snake_case						:     int									=  ids_tensor((self.batch_size, 3)       ,      config.vocab_size							)
														__snake_case						:     Optional[Any]									=  ids_tensor((self.batch_size, 3)       ,      vocab_size=2							)

														# append to next input_ids and
														__snake_case						:     int									=  torch.cat([input_ids, next_tokens]       ,      dim=-1							)
														__snake_case						:     Union[str, Any]									=  torch.cat([input_mask, next_mask]       ,      dim=-1							)

														__snake_case						:     str									=  model(
														    __UpperCamelCase       ,      attention_mask=__UpperCamelCase       ,      encoder_hidden_states=__UpperCamelCase       ,      encoder_attention_mask=__UpperCamelCase       ,      output_hidden_states=__UpperCamelCase       ,      )["hidden_states"][0]
														__snake_case						:     int									=  model(
														    __UpperCamelCase       ,      attention_mask=__UpperCamelCase       ,      encoder_hidden_states=__UpperCamelCase       ,      encoder_attention_mask=__UpperCamelCase       ,      past_key_values=__UpperCamelCase       ,      output_hidden_states=__UpperCamelCase       ,      )["hidden_states"][0]

														# select random slice
														__snake_case						:     str									=  ids_tensor((1,)       ,      output_from_past.shape[-1]							).item()
														__snake_case						:     Union[str, Any]									=  output_from_no_past[:, -3:, random_slice_idx].detach()
														__snake_case						:     List[Any]									=  output_from_past[:, :, random_slice_idx].detach()

														self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1]							)

														# test that outputs are equal for slice
														self.parent.assertTrue(torch.allclose(__UpperCamelCase       ,      __UpperCamelCase       ,      atol=1E-3							)							)






							def        __snake_case   (				self			:    List[str]							)      ->       str:
														__snake_case						:     Tuple									=  self.prepare_config_and_inputs()
														(
														    (
														    __snake_case
														)					,     (
														    __snake_case
														)					,     (
														    __snake_case
														)					,     (
														    __snake_case
														)					,     (
														    __snake_case
														)					,     (
														    __snake_case
														)					,     (
														    __snake_case
														)					,     
														)						:     Dict									=  config_and_inputs
														__snake_case						:     Optional[Any]									=  {"input_ids": input_ids, "attention_mask": input_mask}
														return config, inputs_dict

@require_torch
class 							a						(_UpperCAmelCase	,							_UpperCAmelCase	,							_UpperCAmelCase	,							unittest.TestCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Union[str, Any]	    =    (
							    (
							        FalconModel,
							        FalconForCausalLM,
							        FalconForSequenceClassification,
							        FalconForTokenClassification,
							        FalconForQuestionAnswering,
							    )
							    if is_torch_available()
							    else ()
							)
							__UpperCAmelCase       :				List[str]	    =    (FalconForCausalLM,) if is_torch_available() else ()
							__UpperCAmelCase       :				List[str]	    =    (
							    {
							        'feature-extraction': FalconModel,
							        'text-classification': FalconForSequenceClassification,
							        'text-generation': FalconForCausalLM,
							        'question-answering': FalconForQuestionAnswering,
							        'token-classification': FalconForTokenClassification,
							        'zero-shot': FalconForSequenceClassification,
							    }
							    if is_torch_available()
							    else {}
							)
							__UpperCAmelCase       :				Any	    =    False
							__UpperCAmelCase       :				Dict	    =    False

							def        __snake_case   (				self			:    Union[str, Any]							)      ->       Any:
														__snake_case						:     Tuple									=  FalconModelTester(self							)
														__snake_case						:     Dict									=  ConfigTester(self       ,      config_class=__UpperCamelCase       ,      hidden_size=37							)

							def        __snake_case   (				self			:    List[Any]							)      ->       List[str]:
														self.config_tester.run_common_tests()

							def        __snake_case   (				self			:    Optional[Any]							)      ->       Optional[Any]:
														__snake_case						:     Optional[Any]									=  self.model_tester.prepare_config_and_inputs()
														self.model_tester.create_and_check_model(*__UpperCamelCase							)

							def        __snake_case   (				self			:    int							)      ->       Optional[Any]:
														__snake_case					,     *__snake_case						:     Dict									=  self.model_tester.prepare_config_and_inputs()
														for alibi in [True, False]:
																					__snake_case						:     Optional[Any]									=  alibi
																					self.model_tester.create_and_check_model(__UpperCamelCase       ,      *__UpperCamelCase							)

							def        __snake_case   (				self			:    List[str]							)      ->       Optional[int]:
														__snake_case					,     __snake_case						:     Union[str, Any]									=  self.model_tester.prepare_config_and_inputs_for_common()
														__snake_case						:     Union[str, Any]									=  3
														__snake_case						:     Any									=  input_dict["input_ids"]
														__snake_case						:     List[str]									=  input_ids.ne(1							).to(__UpperCamelCase							)
														__snake_case						:     List[str]									=  ids_tensor([self.model_tester.batch_size]       ,      self.model_tester.type_sequence_label_size							)
														__snake_case						:     Tuple									=  FalconForSequenceClassification(__UpperCamelCase							)
														model.to(__UpperCamelCase							)
														model.eval()
														__snake_case						:     List[str]									=  model(__UpperCamelCase       ,      attention_mask=__UpperCamelCase       ,      labels=__UpperCamelCase							)
														self.assertEqual(result.logits.shape       ,      (self.model_tester.batch_size, self.model_tester.num_labels)							)

							def        __snake_case   (				self			:    List[Any]							)      ->       List[Any]:
														__snake_case					,     __snake_case						:     List[str]									=  self.model_tester.prepare_config_and_inputs_for_common()
														__snake_case						:     List[Any]									=  3
														__snake_case						:     Any									=  "single_label_classification"
														__snake_case						:     Optional[Any]									=  input_dict["input_ids"]
														__snake_case						:     Any									=  input_ids.ne(1							).to(__UpperCamelCase							)
														__snake_case						:     List[str]									=  ids_tensor([self.model_tester.batch_size]       ,      self.model_tester.type_sequence_label_size							)
														__snake_case						:     List[str]									=  FalconForSequenceClassification(__UpperCamelCase							)
														model.to(__UpperCamelCase							)
														model.eval()
														__snake_case						:     Optional[Any]									=  model(__UpperCamelCase       ,      attention_mask=__UpperCamelCase       ,      labels=__UpperCamelCase							)
														self.assertEqual(result.logits.shape       ,      (self.model_tester.batch_size, self.model_tester.num_labels)							)

							def        __snake_case   (				self			:    List[str]							)      ->       Optional[Any]:
														__snake_case					,     __snake_case						:     List[str]									=  self.model_tester.prepare_config_and_inputs_for_common()
														__snake_case						:     Any									=  input_dict["input_ids"]
														__snake_case						:     Optional[Any]									=  FalconForCausalLM(__UpperCamelCase							)
														model.to(__UpperCamelCase							)
														model.eval()
														__snake_case						:     int									=  model(__UpperCamelCase       ,      use_cache=__UpperCamelCase							)
														__snake_case						:     Any									=  input_ids.shape[0]
														__snake_case						:     Optional[Any]									=  model._convert_to_rw_cache(result.past_key_values							)
														__snake_case						:     Tuple									=  model._convert_cache_to_standard_format(__UpperCamelCase       ,      __UpperCamelCase							)
														for layer in range(len(__UpperCamelCase							)							):
																					for tensor_idx in range(2							):
																												self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3							)
																												self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4							)
																												self.assertTrue(
																												    torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx]							)							)

							def        __snake_case   (				self			:    Optional[int]							)      ->       Union[str, Any]:
														__snake_case					,     __snake_case						:     Optional[Any]									=  self.model_tester.prepare_config_and_inputs_for_common()
														__snake_case						:     Optional[Any]									=  3
														__snake_case						:     str									=  "multi_label_classification"
														__snake_case						:     List[Any]									=  input_dict["input_ids"]
														__snake_case						:     Union[str, Any]									=  input_ids.ne(1							).to(__UpperCamelCase							)
														__snake_case						:     List[Any]									=  ids_tensor(
														    [self.model_tester.batch_size, config.num_labels]       ,      self.model_tester.type_sequence_label_size							).to(torch.float							)
														__snake_case						:     Optional[Any]									=  FalconForSequenceClassification(__UpperCamelCase							)
														model.to(__UpperCamelCase							)
														model.eval()
														__snake_case						:     Dict									=  model(__UpperCamelCase       ,      attention_mask=__UpperCamelCase       ,      labels=__UpperCamelCase							)
														self.assertEqual(result.logits.shape       ,      (self.model_tester.batch_size, self.model_tester.num_labels)							)






							def        __snake_case   (				self			:    int							)      ->       Union[str, Any]:






														for model_class in self.all_generative_model_classes:
																					__snake_case					,     __snake_case						:     Dict									=  self.model_tester.prepare_config_and_inputs_for_common()

																					# If it doesn't support cache, pass the test
																					if not hasattr(__UpperCamelCase       ,      "use_cache"							):
																												return

																					__snake_case						:     Optional[Any]									=  model_class(__UpperCamelCase							).to(__UpperCamelCase							)
																					if "use_cache" not in inputs:
																												__snake_case						:     int									=  True
																					__snake_case						:     Any									=  model(**__UpperCamelCase							)

																					# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
																					if "past_key_values" not in outputs:
																												return

																					__snake_case						:     Tuple									=  (
																					    getattr(__UpperCamelCase       ,      "decoder_layers"       ,      __UpperCamelCase							)
																					    or getattr(__UpperCamelCase       ,      "num_decoder_layers"       ,      __UpperCamelCase							)
																					    or config.num_hidden_layers
																					)
																					__snake_case						:     Union[str, Any]									=  getattr(__UpperCamelCase       ,      "num_kv_heads"       ,      config.num_attention_heads							)
																					__snake_case						:     int									=  getattr(__UpperCamelCase       ,      "d_model"       ,      config.hidden_size							)
																					__snake_case						:     int									=  embed_dim // num_attention_heads

																					__snake_case						:     Union[str, Any]									=  outputs["past_key_values"]
																					self.assertEqual(len(__UpperCamelCase							)       ,      __UpperCamelCase							)

																					__snake_case					,     __snake_case						:     str									=  inputs["input_ids"].shape
																					for i in range(__UpperCamelCase							):
																												if config.new_decoder_architecture:
																																			__snake_case						:     Union[str, Any]									=  config.num_attention_heads
																												elif config.multi_query:
																																			__snake_case						:     Union[str, Any]									=  1
																												self.assertEqual(len(past_kv[0]							)       ,      2							)  # K V for the decoder = 2
																												self.assertEqual(
																												    past_kv[i][0].shape       ,      (batch_size, num_attention_heads, seq_length, per_head_embed_dim)							)
																												self.assertEqual(
																												    past_kv[i][1].shape       ,      (batch_size, num_attention_heads, seq_length, per_head_embed_dim)							)

@require_torch
class 							a						(unittest.TestCase ):




							"""simple docstring"""


							@slow
							def        __snake_case   (				self			:    Optional[Any]							)      ->       int:
														__snake_case						:     int									=  AutoTokenizer.from_pretrained("Rocketknight1/falcon-rw-1b"							)
														__snake_case						:     Any									=  FalconForCausalLM.from_pretrained("Rocketknight1/falcon-rw-1b"							)
														model.eval()
														model.to(__UpperCamelCase							)
														__snake_case						:     Union[str, Any]									=  tokenizer("My favorite food is"       ,      return_tensors="pt"							).to(__UpperCamelCase							)

														__snake_case						:     Tuple									=  (
														    "My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."
														)

														__snake_case						:     Any									=  model.generate(**__UpperCamelCase       ,      do_sample=__UpperCamelCase       ,      max_new_tokens=19							)
														__snake_case						:     Optional[int]									=  tokenizer.batch_decode(__UpperCamelCase							)[0]

														self.assertEqual(__UpperCamelCase       ,      __UpperCamelCase							)

							@slow
							def        __snake_case   (				self			:    List[Any]							)      ->       Tuple:






														for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
																					__snake_case						:     List[Any]									=  AutoTokenizer.from_pretrained(__UpperCamelCase							)
																					__snake_case						:     Optional[int]									=  FalconForCausalLM.from_pretrained(__UpperCamelCase							)
																					model.eval()
																					model.to(__UpperCamelCase							)
																					__snake_case						:     Optional[int]									=  tokenizer("My favorite food is"       ,      return_tensors="pt"							).to(__UpperCamelCase							)

																					# We just test that these run without errors - the models are randomly initialized
																					# and so the actual text outputs will be garbage
																					model.generate(**__UpperCamelCase       ,      do_sample=__UpperCamelCase       ,      max_new_tokens=4							)
																					model.generate(**__UpperCamelCase       ,      do_sample=__UpperCamelCase       ,      max_new_tokens=4							)
																					model.generate(**__UpperCamelCase       ,      num_beams=2       ,      max_new_tokens=4							)






							@slow
							def        __snake_case   (				self			:    List[str]							)      ->       Any:






														with torch.no_grad():
																					for repo in [
																					    "Rocketknight1/falcon-rw-1b",
																					    "Rocketknight1/tiny-random-falcon-7b",
																					    "Rocketknight1/tiny-random-falcon-40b",
																					]:
																												__snake_case						:     Any									=  AutoTokenizer.from_pretrained(__UpperCamelCase							)
																												__snake_case						:     Union[str, Any]									=  FalconForCausalLM.from_pretrained(__UpperCamelCase							)
																												model.eval()
																												model.to(device=__UpperCamelCase							)
																												__snake_case						:     List[Any]									=  tokenizer("My favorite food is"       ,      return_tensors="pt"							).to(__UpperCamelCase							)

																												# Test results are the same with and without cache
																												__snake_case						:     Optional[Any]									=  model.generate(**__UpperCamelCase       ,      do_sample=__UpperCamelCase       ,      max_new_tokens=20       ,      use_cache=__UpperCamelCase							)
																												__snake_case						:     Optional[Any]									=  model.generate(**__UpperCamelCase       ,      do_sample=__UpperCamelCase       ,      max_new_tokens=20       ,      use_cache=__UpperCamelCase							)
																												self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0							)



"""simple docstring"""



import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase        = OmegaConf.load(_A   )
    if display:
        print(yaml.dump(OmegaConf.to_container(_A   )   )   )
    return config


def 			_UpperCamelCase						(    _A       ,   _A=None       ,   _A=None   )     ->	List[Any]:





    """simple docstring"""

    if conf_path is None:
        _UpperCAmelCase        = """./model_checkpoints/vqgan_only.yaml"""
    _UpperCAmelCase        = load_config(_A       ,   display=_A   )
    _UpperCAmelCase        = VQModel(**config.model.params   )
    if ckpt_path is None:
        _UpperCAmelCase        = """./model_checkpoints/vqgan_only.pt"""
    _UpperCAmelCase        = torch.load(_A       ,   map_location=_A   )
    if ".ckpt" in ckpt_path:
        _UpperCAmelCase        = sd["""state_dict"""]
    model.load_state_dict(_A       ,   strict=_A   )
    model.to(_A   )
    del sd
    return model


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = model.encode(_A   )
    print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""   )
    _UpperCAmelCase        = model.decode(_A   )
    return xrec


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = string.rsplit("""."""       ,   1   )
    if reload:
        _UpperCAmelCase        = importlib.import_module(_A   )
        importlib.reload(_A   )
    return getattr(importlib.import_module(_A       ,   package=_A   )       ,   cls   )


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    if "target" not in config:
        raise KeyError("""Expected key `target` to instantiate."""   )
    return get_obj_from_str(config["""target"""]   )(**config.get("""params"""       ,   {}   )   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=True       ,   _A=True   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = instantiate_from_config(_A   )
    if sd is not None:
        model.load_state_dict(_A   )
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}







def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A   )     ->	Optional[Any]:





    """simple docstring"""

    if ckpt:
        _UpperCAmelCase        = torch.load(_A       ,   map_location="""cpu"""   )
        _UpperCAmelCase        = pl_sd["""global_step"""]
        print(F"""loaded model from global step {global_step}."""   )
    else:
        _UpperCAmelCase        = {"""state_dict""": None}
        _UpperCAmelCase        = None
    _UpperCAmelCase        = load_model_from_config(config.model       ,   pl_sd["""state_dict"""]       ,   gpu=_A       ,   eval_mode=_A   )["""model"""]
    return model, global_step